Home Journals Articles Updates About
CN
image
收藏切换
Method of coupling identifying unsafe behaviors of underground personnel based on dual-model algorithm
收藏切换
PDF
Bo TAN1, Longkun SUI**, 1, Wei KE2, Yan LIU2, Quanjie ZHU3, Ning HE3
China Safety Science Journal | 2026, 36(2) : 18 - 26
Less
收藏切换
China Safety Science Journal | 2026, 36(2): 18-26
• Safety Science Theories and Methods •
Method of coupling identifying unsafe behaviors of underground personnel based on dual-model algorithm
Full
Bo TAN1, Longkun SUI**, 1, Wei KE2, Yan LIU2, Quanjie ZHU3, Ning HE3
Affiliations
  • 1School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
  • 2Shiyan Company of Hubei Tobacco Company, Shiyan Hubei 442000, China
  • 3School of Emergency Technology and Management, North China Institute of Science and Technology, Sanhe Hebei 065201, China
Published: 2026-02-28 doi: 10.16265/j.cnki.issn1003-3033.2026.02.1372
Outline
收藏切换

In order to prevent safety accidents caused by unsafe behaviors of underground personnel and to ensure their safety, by utilizing advanced machine vision and computer technologies, the traditional YOLOv5s algorithm and OpenPose algorithm target detection models were improved, and a dual-model coupled algorithm for identifying unsafe behaviors of underground personnel was proposed. Through statistical analysis of the most common unsafe behaviors in current underground coal mines, the unsafe behaviors of miners were classified, including item-related, action-related, and area-related unsafe behaviors. According to the characteristics of miners' unsafe behaviors, the improved YOLOv5s algorithm and the OpenPose algorithm were coupled for recognition, and training and verification were conducted on public datasets and self-built datasets. The results show that compared with the current mainstream methods, the dual-model coupled recognition method has a significant improvement in recognition accuracy on self-built datasets and public datasets, with an increase of 5% to 10%, and can quickly and effectively identify unsafe behaviors of underground personnel.

underground personnel  /  unsafe behaviors  /  dual-model algorithm  /  coupling identifying  /  object detection
Bo TAN, Longkun SUI, Wei KE, Yan LIU, Quanjie ZHU, Ning HE. Method of coupling identifying unsafe behaviors of underground personnel based on dual-model algorithm[J]. China Safety Science Journal, 2026 , 36 (2) : 18 -26 . DOI: 10.16265/j.cnki.issn1003-3033.2026.02.1372
Appendix
Less
Year 2026 volume 36 Issue 2
PDF
88
27
Cite this Article
BibTeX
Article Info
doi: 10.16265/j.cnki.issn1003-3033.2026.02.1372
  • Receive Date：2025-09-10
  • Online Date：2026-07-08
  • Published：2026-02-28
Article Data
Affiliations
History
  • Received：2025-09-10
  • Revised：2025-11-18
Funding
Affiliations
    1School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
    2Shiyan Company of Hubei Tobacco Company, Shiyan Hubei 442000, China
    3School of Emergency Technology and Management, North China Institute of Science and Technology, Sanhe Hebei 065201, China
References
Share
https://castjournals.cast.org.cn/joweb/zgaqkxxb/EN/10.16265/j.cnki.issn1003-3033.2026.02.1372
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House